nvidia-cosmos/cosmos-rl
Jan 2026 – Mar 2026
3 Months active
Languages Used
Python
Technical Skills
Deep LearningMachine LearningPythonalgorithm optimizationdeep learningmachine learning
sunshk
PROFILE
Sunshk
Worked on the nvidia-cosmos/cosmos-rl repository to enhance deep learning model training workflows, focusing on configurability and reliability. Delivered features such as step-based stopping and epsilon configurability for the SFTTrainer, enabling more flexible and reproducible training runs. Integrated PI05 model support with improved data processing and multi-dataset handling, streamlining experimentation and deployment. Addressed critical bugs by refining post-training weight uploads using asynchronous programming and ensuring compatibility of PI05 models within the Libero environment. Leveraged Python, PyTorch, and reinforcement learning techniques to optimize algorithm performance and data handling, resulting in more robust, scalable, and maintainable machine learning pipelines across the project.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
8Total
Bugs
2
Commits
8
Features
2
Lines of code
2,734
Activity Months3
Your Network
1647 peopleSame Organization
@nvidia.com1624
Work History
March 2026
1 Commits
Mar 1, 2026Stabilized PI05 model workflow in Libero by fixing compatibility issues and tightening training configuration. No new features shipped this month; major bug fix improves testing framework and ensures PI05 models train reliably in Libero. This reduces integration risk and accelerates model iteration by providing a consistent setup in CI.
1 Commits
Mar 1, 2026Stabilized PI05 model workflow in Libero by fixing compatibility issues and tightening training configuration. No new features shipped this month; major bug fix improves testing framework and ensures PI05 models train reliably in Libero. This reduces integration risk and accelerates model iteration by providing a consistent setup in CI.
March 2026
February 2026
5 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for nvidia-cosmos/cosmos-rl. Focused on delivering end-to-end PI05 model support within the SFT framework, improving data handling across multiple datasets, and hardening the training workflow to ensure reliable deployment of trained models.
February 2026
5 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for nvidia-cosmos/cosmos-rl. Focused on delivering end-to-end PI05 model support within the SFT framework, improving data handling across multiple datasets, and hardening the training workflow to ensure reliable deployment of trained models.
January 2026
2 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for nvidia-cosmos/cosmos-rl: Delivered training configurability enhancements for SFTTrainer, including epsilon configurability and step-based stopping, enabling flexible budgets and preventing epoch-only overruns. Implemented targeted fixes to epsilon control and max-step budgeting to improve training reliability and reproducibility across experiments.
2 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for nvidia-cosmos/cosmos-rl: Delivered training configurability enhancements for SFTTrainer, including epsilon configurability and step-based stopping, enabling flexible budgets and preventing epoch-only overruns. Implemented targeted fixes to epsilon control and max-step budgeting to improve training reliability and reproducibility across experiments.
January 2026
Activity
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Quality Metrics
Correctness87.6%
Maintainability85.0%
Architecture82.6%
Performance82.6%
AI Usage37.6%
Skills & Technologies
Programming Languages
Python
Technical Skills
Asynchronous ProgrammingData ProcessingData ScienceDeep LearningMachine LearningModel TrainingPyTorchPythonPython DevelopmentReinforcement LearningSoftware Developmentalgorithm optimizationdata handlingdeep learningmachine learning
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